Plate Number Recognition based on Hybrid Techniques

In today's world automatic license plate recognition play an important role in monitoring and organizing vehicles. In this paper, we propose a method of detecting and recognizing the license plates of vehicles in an automatic way in our country. The system can be used to collect toll, in car parking and find stolen vehicles. We have used different image processing techniques like resizing image, image binarization, connected component analysis, image enhancement and noise filtering. Our system is composed of four main modules, such as detection of the license plate area, extraction of license plate. Then, segmentation of characters and words and finally recognition of the characters and words. As Bangladesh Road Transport Authority (BRTA) imposed a common standard for vehicle license plate, the size and aspect ratio of all license plates are same. We have used a threshold value to detect and extract the license plate based on our analysis. Afterward, for character segmentation, we used connected components. Later, we used deep learning tool ‘the Convolutional Neural Network’ for character recognition. Due to the lack of a standard data set, we have developed a customized dataset of Bangladeshi number plate for the implementation of our method. The accuracy of our proposed system is 95.42%.

The identification and recognition of automatic license plates (ALP) are critical for traffic surveillance, parking management, and preserving the rhythm of modern urban life. In this paper, a deep learning-based method is proposed for ALP. In the proposed work, the license plate region is initially segmented in a given vehicle image, and the plate number and city region are extracted from the segmented license plate region. Residual neural networks (ResNet) architecture-based deep feature extraction is considered. The fully connected layer of the ResNet model is used to obtain the deep features for the cropped Arabic numbers and city regions, respectively. The extracted features are fed into the sequential input layer of the Long Short-Term Memory (LSTM) classifier. Various experiments are carried out on a dataset that was collected in the northern Iraq region and the classification accuracy score is used for performance evaluation. According to the obtained results, the proposed method is effective where the calculated accuracy scores were 98.51% and 100% for Arabic numbers and city regions, respectively. The performance comparison of the proposed method with some of the existing methods indicates the high performance of the proposed study.

On the streets of Taiwan, many roadside tollers are often seen riding motorcycles in one hand, and the other hand holding mobile devices to issue payment notices for cars and motorcycles parked on the roadsides. The work of roadside tollers is very dangerous. First, they must first park their motorcycles next to the roadside cars and motorcycles. They then use their eyes to confirm the license plate number, enter the license plate number into the mobile device, and finally place the bill on the car’s windows or attach the bill to the motorcycles. Our idea is to implement an automated license plate recognition system in mobile devices to increase the efficiency of roadside tollers and reduce their time on the road. Recently, license plate recognition systems have been widely used in various aspects of life, such as parking lot toll systems, access management systems, and traffic management systems. However, existing license plate recognition systems must have good recognition rates under a number of constraints, such as fixed angles and fixed light sources. Moreover, due to the insufficient computing resources of the general mobile device, the application cannot have a good recognition rate in the complex environment or skewed angle in the license plate recognition. Therefore, this paper proposes a lightweight and high-performance multi-angle license plate character recognition model, which reduces the complexity and computational complexity of traditional license plate recognition. This paper also collects a large number of license plate images from different environments, angles and sizes as training data. Finally, we propose an optimized deep learning model to identify the characters on license plates. The experimental results show that the proposed model can recognize the license plate with a tilt of 0~60 degrees, and the overall recall rate is 84.5%. Compared with Tiny-YOLOv2, the computation of the proposed model is reduced by 61% with a little penalty of recall.

Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software’s features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. Computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community’s methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as “extraction,” “pre-processing,” and “segmentation,” and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception V3, and ResNet 50 have all been put through their paces in this regard.

Automatic License Plate Recognition (ALPR) is a growing research area as with the increase in the number of vehicles, requirements for high accuracy ALPR systems are increasing as it has been extensively used for automatic road tax collection, monitor unlike activities etc. Character segmentation is a major step in the ALPR system. This paper includes the use of image pre-processing techniques, K-means clustering, Otsu and Niblack method and Connected Component Analysis (CCA) to perform character segmentation. Unlike existing works which are based on license plates of other countries and therefore may not perform well on Indian license plates, in this work we have dealt with Indian license plates. Many of the existing works are based on license plates that are captured from a straight angle and good illumination and hence may not perform well if the images of license plates have noises or are captured at different angles. Therefore, in this work we have used license plates which are at different angles with different lighting conditions.

The intent of this research is to design a license plate recognition (LPR) system in the domain of Bangla language for smart vehicle management. The proposed system is designed on the basis of computer vision tools and deep supervised machine learning model. The system has three modules: license plate detection, character segmentation and recognition of the characters of the License Plate (LP). The goal of detection is to localize the plate area from the vehicle image and to crop region of interest (LP). It is executed by applying following process: preprocessing the image, conversion to binary image, contour detection and filtering the contours to get the LP's character contours, tilt correction and cropping the plate area from the image. Then, the cropped LP is segmented to extract the characters from the plate. Finally, the recognition step classifies the characters by means of deep convolution neural network where the features of the character are crafted and learned by the convolution layers of the networks. The system is implemented in Python OpenCV environment for offline car license plates images which are taken in different illuminations, road scenarios and colored cars. The system performance is evaluated in terms of detection rate, segmentation rate, recognition rate and execution time. The results illustrate that the performance of the system is remarkable.

Automatic license plate recognition (ALPR) is a process of identification of the license plate from an image taken at any signal stop or parking station. The Identified information or the number plate can be used for keeping a check on signal stops or for monetary settlements at parking lots or toll stations. It can also be used around the world for law and administrative works like checking a vehicle if it is registered or not. It also finds its use in safety supervision system. The ALPR can be used upon various types of pictures like color images, black and white images and etc. ALPR may fail a number of times if the quality of the picture is not up to the mark. So, ALPR depend a lot in the image quality, and hence the challenges in its real-life application. It may be used both indoors and outdoors but it must be able to process the images both quickly and successfully in all sorts of environments, be it daytime or night time, sunny or rainy. Our proposed method mainly has three modules: 1) Detection of license plate 2) Segmentation of Characters 3) Text Box Generation. The number plates sometimes may be unclear for the cameras. It may sometimes have dirt and scratches on it and thus increasing the complexities of the ALPR system. Today nationwide ALPR has been adopted by several law and governmental agencies for its efficiency and usefulness. Public safety agencies are using the ALPR system to gather and store relevant information about people and to keep a check on the vehicular activities. It is becoming a significant tool for the Law Enforcement Agencies.

With the increasing advancement of science and technology, the popularity of automobiles in my country has gradually increased. Among them, license plate positioning and license plate recognition technology are very important key technologies in the license plate recognition system. Through the license plate positioning technology, the position information, centroid and other data information of the license plate can be obtained, so as to realize the positioning of the license plate. The license plate recognition technology can extract the uniqueness of the vehicle. Identifying the license plate number, so the design and implementation of vehicle recognition and license plate recognition technology based on embedded devices have practical significance and practical application value to promote the technological progress of intelligent transportation and vehicle recognition systems. This design is a license plate recognition system, an embedded character segmentation recognition processing platform with ARM Cortex-A9 as the core, and the method of cross-compiling OpenCV, Qt, BootLoader in the Linux operating system, and transplanting related drivers to the ARM Cortex-A9 platform; Based on the classic algorithm, the OpenCV library function is used to realize the segmentation and recognition of the license plate image characters. The experimental results show that the design has achieved the expected goal.

As the difference of gray value between the numbers and letters on the license plate and the license plate background is very big. We first use region growing algorithm based on the gray value to separate each number and letter into different area. Secondly, we search for the bounding rectangles of all regions and find some rectangular sequences that meet a special condition. Thirdly, calculate the horizon bias for all rectangles according to the coordination of the up-right point in the rectangle, adjust and get the precise location of each rectangle. Forth, we get the binary image which can separate the numbers and letters on the license plates and the license plate background by the dynamic threshold algorithm. The relation of the maximum width of various license plate numbers and letters and its bounding rectangles helps to count the direction of the vertical bias, then the bias is adjusted. At last we statistic the foreground vertical histogram of the previous results, and get the result of precise vertical position and character segmentation. The experimental results show that this algorithm performs well for location of multi-national license plates.

Automatic License Plate Recognition (ALPR) has been the focus of many\nresearches in the past years. In general, ALPR is divided into the following\nproblems: detection of on-track vehicles, license plates detection, segmention\nof license plate characters and optical character recognition (OCR). Even\nthough commercial solutions are available for controlled acquisition\nconditions, e.g., the entrance of a parking lot, ALPR is still an open problem\nwhen dealing with data acquired from uncontrolled environments, such as roads\nand highways when relying only on imaging sensors. Due to the multiple\norientations and scales of the license plates captured by the camera, a very\nchallenging task of the ALPR is the License Plate Character Segmentation (LPCS)\nstep, which effectiveness is required to be (near) optimal to achieve a high\nrecognition rate by the OCR. To tackle the LPCS problem, this work proposes a\nnovel benchmark composed of a dataset designed to focus specifically on the\ncharacter segmentation step of the ALPR within an evaluation protocol.\nFurthermore, we propose the Jaccard-Centroid coefficient, a new evaluation\nmeasure more suitable than the Jaccard coefficient regarding the location of\nthe bounding box within the ground-truth annotation. The dataset is composed of\n2,000 Brazilian license plates consisting of 14,000 alphanumeric symbols and\ntheir corresponding bounding box annotations. We also present a new\nstraightforward approach to perform LPCS efficiently. Finally, we provide an\nexperimental evaluation for the dataset based on four LPCS approaches and\ndemonstrate the importance of character segmentation for achieving an accurate\nOCR.\n

Automatic license plate recognition (LPR) plays an important role in numerous applications and a number of techniques have been proposed. However, most of them worked under restricted conditions, such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. In this study, as few constraints as possible on the working environment are considered. The proposed LPR technique consists of two main modules: a license plate locating module and a license number identification module. The former characterized by fuzzy disciplines attempts to extract license plates from an input image, while the latter conceptualized in terms of neural subjects aims to identify the number present in a license plate. Experiments have been conducted for the respective modules. In the experiment on locating license plates, 1088 images taken from various scenes and under different conditions were employed. Of which, 23 images have been failed to locate the license plates present in the images; the license plate location rate of success is 97.9%. In the experiment on identifying license number, 1065 images, from which license plates have been successfully located, were used. Of which, 47 images have been failed to identify the numbers of the license plates located in the images; the identification rate of success is 95.6%. Combining the above two rates, the overall rate of success for our LPR algorithm is 93.7%.

Smart cities must have all the important characteristics to achieve their intended goals. Proper traffic management and controlling, increased surveillance and safety, and enhanced management and avoidance of incidents must be the priorities of smart cities. Meanwhile, license plate recognition (LPR) has become the most debatable topic in the research community due to various real-time applications, such as “law enforcement, toll-free processing, access control, and traffic surveillance.” Automated LPR is a technique based on computer vision to recognize vehicles with their number plates. This study discusses various “Deep Learning based LPR” techniques to detect and identify “alphanumeric characters” in number plate. The projected model works on “license based detection” and “character recognition.” This technique uses “Optical Character Recognition (OCR)” technology to detect and extract the alphanumerical numbers from the license plate. This study is based on secondary data collected from various studies conducted on Licence Plate Recognition using various Deep Learning models published in databases like Google Scholar, Science Direct, NCBI, etc. Deep learning has been used widely in applications related to computer vision in recent years with great perfection. It is a great solution for modern and traditional image processing, object detection, and feature extraction issues. It has been widely used in different stages of LPR like character segmentation, license plate recognition, and OCR.

Identification of players in image or videos in the sporting events, and automatic vehicle identification by number plate during its moving state is an approachable but tough task. In NBA basketball games, where pictures can be taken by using multiple cameras, by characterizing the jersey colour with MAP detection, isolating the jerseys with the player numbers, and using template matching. A max-effort method is used to locate the majority of game’s participants. Similarly in automatic vehicle identification by licence plate is quite useful in highway electronic toll collection, border, customs checkpoints, and red-light violations etc. It has two techniques like edge detection method and image processing tools. Additionally, there is another technique that has three main components: extraction of the plate region, character segmentation, and plate character identification. We are discussing all these methods along with their working and how these are providing outcomes for the recognition and tracking of the jersey number and number plate recognition.

License plate recognition usually contains three steps, namely license plate detection/localization, character segmentation and character recognition. When reading characters on a license plate one by one after license plate detection step, it is crucial to accurately segment the characters. The segmentation step may be affected by many factors such as license plate boundaries (frames). The recognition accuracy will be significantly reduced if the characters are not properly segmented. This paper presents an efficient algorithm for character segmentation on a license plate. The algorithm follows the step that detects the license plates using an AdaBoost algorithm. It is based on an efficient and accurate skew and slant correction of license plates, and works together with boundary (frame) removal of license plates. The algorithm is efficient and can be applied in real-time applications. The experiments are performed to show the accuracy of segmentation.

CIS Multilingual License Plate Detection and Recognition Based on Convolutional and Transformer Neural Networks